We propose an appearance-based face recognition method called the
Laplacianface approach. By using locality preserving projections
(LPP), the face images are mapped into a face subspace for analysis.
Different from principal component analysis (PCA) and linear discriminant
analysis (LDA) which effectively see only the Euclidean structure
of face space, LPP finds an embedding that preserves local information,
and obtains a face subspace that best detects the essential face
manifold structure. The Laplacianfaces are the optimal linear approximations
to the eigenfunctions of the Laplace Beltrami operator on the face
manifold. In this way, the unwanted variations resulting from changes
in lighting, facial expression, and pose may be eliminated or reduced.
Theoretical analysis shows that PCA, LDA, and LPP can be obtained
from different graph models. We compare the proposed Laplacianface
approach with Eigenface and Fisherface methods on three different
face data sets. Experimental results suggest that the proposed Laplacianface
approach provides a better representation and achieves lower error
rates in face recognition.
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